lint recent changes

api/onnx_web/chain/blend_img2img.py

@@ -1,10 +1,10 @@
 from logging import getLogger
+from typing import Optional
 
 import numpy as np
 import torch
 from diffusers import OnnxStableDiffusionImg2ImgPipeline
 from PIL import Image
-from typing import Optional
 
 from ..device_pool import JobContext
 from ..diffusion.load import load_pipeline

api/onnx_web/chain/correct_gfpgan.py

@@ -1,10 +1,10 @@
 from logging import getLogger
 from os import path
+from typing import Optional
 
 import numpy as np
 from gfpgan import GFPGANer
 from PIL import Image
-from typing import Optional
 
 from ..device_pool import JobContext
 from ..params import DeviceParams, ImageParams, StageParams, UpscaleParams
@@ -18,7 +18,12 @@ last_pipeline_instance: Optional[GFPGANer] = None
 last_pipeline_params: Optional[str] = None
 
 
-def load_gfpgan(server: ServerContext, stage: StageParams, upscale: UpscaleParams, device: DeviceParams):
+def load_gfpgan(
+    server: ServerContext,
+    stage: StageParams,
+    upscale: UpscaleParams,
+    device: DeviceParams,
+):
     global last_pipeline_instance
     global last_pipeline_params
 

api/onnx_web/upscale.py

@@ -35,7 +35,9 @@ def run_upscale_correction(
 
     if upscale.scale > 1:
         if "esrgan" in upscale.upscale_model:
-            resr_stage = StageParams(tile_size=stage.tile_size, outscale=upscale.outscale)
+            resr_stage = StageParams(
+                tile_size=stage.tile_size, outscale=upscale.outscale
+            )
             chain.append((upscale_resrgan, resr_stage, None))
         elif "stable-diffusion" in upscale.upscale_model:
             mini_tile = min(SizeChart.mini, stage.tile_size)
@@ -45,7 +47,9 @@ def run_upscale_correction(
             logger.warn("unknown upscaling model: %s", upscale.upscale_model)
 
     if upscale.faces:
-        face_stage = StageParams(tile_size=stage.tile_size, outscale=upscale.face_outscale)
+        face_stage = StageParams(
+            tile_size=stage.tile_size, outscale=upscale.face_outscale
+        )
         if "codeformer" in upscale.correction_model:
             chain.append((correct_codeformer, face_stage, None))
         elif "gfpgan" in upscale.correction_model: